Systems, Methods, and Apparatuses for Audience Metric Determination

1. A non-transitory computer readable medium storing processor executable instructions that, when executed by at least one processor, cause the at least one processor to: determine, based on a quantity of time associated with output of a type of content, a plurality of subsets of a plurality of devices; determine, for each subset of the plurality of subsets, based on different levels of viewership associated with each subset of the plurality of subsets, a weight; determine, based on the weight for each subset of the plurality of subsets, an index parameter indicative of a likelihood of a device of the subset of the plurality of devices to output the type of content; generate, based on the index parameter, data indicative of a plurality of content time slots; and select, based on the data indicative of the plurality of content time slots, a pool of the plurality of devices, wherein the selection comprises a quantity of devices from each subset of the plurality of subsets.

2. The non-transitory computer readable medium of claim 1, wherein the processor executable instructions that cause the at least one processor to determine the plurality of subsets further cause the at least one processor to determine at least one subset threshold, wherein the plurality of subsets comprises a first subset associated with a first quantity of output content that is above the at least one subset threshold, a second subset associated with a second quantity of output content that is below the at least one subset threshold, and a third subset associated with a third quantity of output content that is below the second quantity of output content.

3. The non-transitory computer readable medium of claim 1, wherein the processor executable instructions that cause the at least one processor to determine the plurality of subsets further cause the at least one processor to cluster, based on audience type and viewership data, the plurality of devices, wherein the audience type comprises at least one of: a genre audience type, a sports content audience type, a cooking content audience type, a chronological age range audience type, a content network audience type, or a content program audience type.

4. The non-transitory computer readable medium of claim 1, wherein the processor executable instructions that cause the at least one processor to determine the plurality of subsets further cause the at least one processor to determine, based on at least one subset threshold, a first portion of the plurality of devices in a first subset and a second portion of the plurality of devices in a second subset.

5. The non-transitory computer readable medium of claim 1, wherein the processor executable instructions that cause the at least one processor to determine the weight for each subset of the plurality of subsets of the plurality of devices further cause the at least one processor to: determine a first average distance of each device in a first subset to a midpoint of the first subset; and determine a second average distance of each device in a second subset to the midpoint of the first subset.

6. The non-transitory computer readable medium of claim 1, wherein the processor executable instructions that cause the at least one processor to determine the index parameter further cause the at least one processor to apply, to corresponding index values, the weight for each subset of the plurality of subsets of the plurality of devices.

7. The non-transitory computer readable medium of claim 1, wherein the processor executable instructions that cause the at least one processor to determine the index parameter further cause the at least one processor to determine a ratio of a portion of the plurality of devices that output the type of content item and the plurality of devices that output the type of content item during the plurality of content time slots, wherein the portion of the plurality of devices correspond to a target device subset.

8. The non-transitory computer readable medium of claim 1, wherein the processor executable instructions that cause the at least one processor to generate the data further cause the at least one processor to determine, based on the index parameter, a ranking of the plurality of content time slots, wherein the ranking of the plurality of content time slots comprises a highest ranked content time slot.

9. The non-transitory computer readable medium of claim 1, wherein the processor executable instructions that cause the at least one processor to generate the data further cause the at least one processor to generate a matrix indicative of at least one of: a plurality of content items or a plurality of content channels being output during the plurality of content time slots.

10. The non-transitory computer readable medium of claim 1, wherein the plurality of devices are associated with at least one of: output of content, selection of a content type, or content viewership.

11. The non-transitory computer readable medium of claim 1, wherein the likelihood of the device to output the type of content is associated with at least one of: a quantity of devices of the subset that output the type of content item during a content time slot of the plurality of content time slots or a likelihood that a device of the quantity of devices that outputs the type of content item during the content time slot is clustered into the subset.

12. The non-transitory computer readable medium of claim 1, wherein the processor executable instructions further cause the at least one processor to determine a daypart classification of the plurality of content time slots, wherein the daypart classification comprises at least one of: early morning, daytime, fringe, late fringe, overnight, or primetime.

13. The non-transitory computer readable medium of claim 1, the processor executable instructions further cause the at least one processor to determine an average distance between points of each subset of the plurality of subsets and a center point of a subset of the plurality of subsets of the plurality of devices.

14. A non-transitory computer readable medium storing processor executable instructions that, when executed by at least one processor, cause the at least one processor to: receive an indication of a type of content and a request for a ranking of a plurality of content time slots; determine, based on a weight for each device of a subset of a plurality of subsets of devices, an index parameter indicative of a likelihood of a device of the plurality of subsets of devices to output the type of content; and send, based on the index parameter, data indicative of the plurality of content time slots.

15. The non-transitory computer readable medium of claim 14, wherein the processor executable instructions that cause the at least one processor to receive the request further cause the at least one processor to receive, from a user device, an indication of a target device subset corresponding to the type of content, wherein the type of content comprises at least one of: a sports content item, an drama content item, a content item for a chronological age range, a genre content item, or a content item title.

16. The non-transitory computer readable medium of claim 14, wherein the processor executable instructions that cause the at least one processor to receive the request further cause the at least one processor to receive, from a user device, an indication of the plurality of subsets of devices, wherein the plurality of subsets of devices comprises: a first subset associated with a first quantity of output content that is above at least one subset threshold, a second subset associated with a second quantity of output content that is below the at least one subset threshold, and a third subset associated with a third quantity of output content that is below the second quantity of output content.

17. The non-transitory computer readable medium of claim 14, wherein the processor executable instructions that cause the at least one processor to determine the index parameter further cause the at least one processor to determine a ratio of a portion of the plurality of subsets of devices associated with the likelihood to output the type of content and a quantity of the plurality of subsets of devices.

18. The non-transitory computer readable medium of claim 14, wherein the processor executable instructions that cause the at least one processor to send the data further cause the at least one processor to send, based on the type of content, at least one of: a matrix, a list, a spreadsheet, or a table.

19. The non-transitory computer readable medium of claim 14, wherein the processor executable instructions that cause the at least one processor to send the data further cause the at least one processor to send an indication of a highest ranked content time slot of the plurality of content time slots.

20. The non-transitory computer readable medium of claim 14, wherein the processor executable instructions that cause the at least one processor to send the data further cause the at least one processor to send an indication of a lowest ranked content time slot of the plurality of content time slots.

21. The non-transitory computer readable medium of claim 14, wherein each content time slot of the plurality of content time slots defines a time period and a type of content source.

22. The non-transitory computer readable medium of claim 14, wherein the plurality of subsets of devices are associated with at least one of: output of content, selection of a content type, or content viewership.

23. The non-transitory computer readable medium of claim 14, wherein the processor executable instructions further cause the at least one processor to cluster, based on viewership data, a portion of the plurality of subsets of devices to a target device subset of a plurality of target device subsets.

24. The non-transitory computer readable medium of claim 14, wherein the processor executable instructions further cause the at least one processor to determine a weight for each subset of the plurality of subsets of devices based on an average difference between content output times of devices in each subset and a midpoint of a subset of the plurality of subsets of devices, wherein the midpoint comprises a weighted average content output time of the subset and the weight is based on a distance associated with each subset of the plurality of subsets of devices.

25. The non-transitory computer readable medium of claim 14, wherein the processor executable instructions further cause the at least one processor to determine, based on clustering devices according to a clustering algorithm, the plurality of subsets of the devices.

26. A non-transitory computer readable medium storing processor executable instructions that, when executed by at least one processor, cause the at least one processor to: determine a plurality of subsets of a plurality of devices, wherein each subset of the plurality of subsets is associated with a quantity of time associated with output of a type of content; determine, for each subset of the plurality of subsets, based on viewership data associated with each subset of the plurality of subsets, a weight; determine, based on the weight for each subset of the plurality of subsets, an index parameter indicative of a likelihood of a device of the subset of the plurality of devices to output the type of content; and generate, based on the index parameter, data indicative of a ranking of a plurality of content networks.

27. The non-transitory computer readable medium of claim 26, wherein the processor executable instructions that cause the at least one processor to determine the plurality of subsets further cause the at least one processor to apply a k-means clustering algorithm based on at least one of: a level of viewership, a type of content genre, a type of content network, a type of content program, or a type of content title.

28. The non-transitory computer readable medium of claim 26, wherein the processor executable instructions that cause the at least one processor to determine the plurality of subsets further cause the at least one processor to cluster, based on audience type, the plurality of devices, wherein the audience type comprises at least one of: a genre audience type, a sports content audience type, a cooking content audience type, a chronological age range audience type, a content network audience type, or a content program audience type.

29. The non-transitory computer readable medium of claim 26, wherein the processor executable instructions that cause the at least one processor to determine the plurality of subsets further cause the at least one processor to determine at least one subset threshold, wherein the plurality of subsets of the plurality of devices comprises a first subset associated with a first quantity of output content that is above the at least one subset threshold, a second subset associated with a second quantity of output content that is below the at least one subset threshold, and a third subset associated with a third quantity of output content that is below the second quantity of output content.

30. The non-transitory computer readable medium of claim 26, wherein the processor executable instructions that cause the at least one processor to determine the weight for each subset of the plurality of subsets further cause the at least one processor to: determine a first average distance of points of a first subset of the plurality of subsets to a midpoint of the first subset; and determine a second average distance of points of a second subset of the plurality of subsets to the midpoint.

31. The non-transitory computer readable medium of claim 26, wherein the processor executable instructions that cause the at least one processor to determine the index parameter further cause the at least one processor to apply, to corresponding index values, the weight for each subset of the plurality of subsets.

32. The non-transitory computer readable medium of claim 26, wherein the processor executable instructions that cause the at least one processor to determine the index parameter further cause the at least one processor to determine a ratio of a portion of the plurality of devices associated with the likelihood to output the type of content and a quantity of the plurality of devices, wherein the portion of the device is associated with viewership of a target device subset during a content time slot, and wherein the quantity of the plurality of devices is associated with viewership by a general audience during the content time slot.

33. The non-transitory computer readable medium of claim 26, wherein the processor executable instructions that cause the at least one processor to generate the data further cause the at least one processor to generate at least one of: a matrix, a list, a spreadsheet, or a table.

34. The non-transitory computer readable medium of claim 26, wherein the processor executable instructions that cause the at least one processor to generate the data further cause the at least one processor to generate an indication of a content network of the plurality of content networks during a content time slot, wherein the content time slot comprises at least one of: early morning, daytime, fringe, late fringe, overnight, or primetime.

35. The non-transitory computer readable medium of claim 26, wherein the processor executable instructions that cause the at least one processor to generate the data further cause the at least one processor to determine a highest ranked content network of the plurality of content networks.

36. A system comprising: a plurality of devices configured to output content; and a computing device configured to: determine, based on a quantity of time associated with output of a type of content, a plurality of subsets of the plurality of devices; determine, for each subset of the plurality of subsets, based on different levels of viewership associated with each subset of the plurality of subsets, a weight; determine, based on the weight for each subset of the plurality of subsets, an index parameter indicative of a likelihood of a device of the subset of the plurality of devices to output the type of content; generate, based on the index parameter, data indicative of a plurality of content time slots; and select, based on the data indicative of the plurality of content time slots, a pool of the plurality of devices, wherein the selection comprises a quantity of devices from each subset of the plurality of subsets.

37. The system of claim 36, wherein the computing device configured to determine the plurality of subsets further comprises the computing device configured to determine at least one subset threshold, wherein the plurality of subsets comprises a first subset associated with a first quantity of output content that is above the at least one subset threshold, a second subset associated with a second quantity of output content that is below the at least one subset threshold, and a third subset associated with a third quantity of output content that is below the second quantity of output content.

38. The system of claim 36, wherein the computing device configured to determine the plurality of subsets further comprises the computing device configured to cluster, based on audience type and viewership data, the plurality of devices, wherein the audience type comprises at least one of: a genre audience type, a sports content audience type, a cooking content audience type, a chronological age range audience type, a content network audience type, or a content program audience type.

39. The system of claim 36, wherein the computing device configured to determine the plurality of subsets further comprises the computing device configured to determine, based on at least one subset threshold, a first portion of the plurality of devices in a first subset and a second portion of the plurality of devices in a second subset.

40. The system of claim 36, wherein the computing device configured to determine the weight for each subset of the plurality of subsets of the plurality of devices further comprises the computing device configured to: determine a first average distance of each device in a first subset to a midpoint of the first subset; and determine a second average distance of each device in a second subset to the midpoint of the first subset.

41. The system of claim 36, wherein the computing device configured to determine the index parameter further comprises the computing device configured to apply, to corresponding index values, the weight for each subset of the plurality of subsets of the plurality of devices.

42. The system of claim 36, wherein the computing device configured to determine the index parameter further comprises the computing device configured to determine a ratio of a portion of the plurality of devices that output the type of content item and the plurality of devices that output the type of content item during the plurality of content time slots, wherein the portion of the plurality of devices correspond to a target device subset.

43. The system of claim 36, wherein the computing device configured to generate the data further comprises the computing device configured to determine, based on the index parameter, a ranking of the plurality of content time slots, wherein the ranking of the plurality of content time slots comprises a highest ranked content time slot.

44. The system of claim 36, wherein the computing device configured to generate the data further comprises the computing device configured to generate a matrix indicative of at least one of: a plurality of content items or a plurality of content channels being output during the plurality of content time slots.

45. The system of claim 36, wherein the plurality of devices are associated with at least one of: output of content, selection of a content type, or content viewership.

46. The system of claim 36, wherein the likelihood of the device to output the type of content is associated with at least one of: a quantity of devices of the subset that output the type of content item during a content time slot of the plurality of content time slots or a likelihood that a device of the quantity of devices that outputs the type of content item during the content time slot is clustered into the subset.

47. The system of claim 36, wherein the computing device is further configured to determine a daypart classification of the plurality of content time slots, wherein the daypart classification comprises at least one of: early morning, daytime, fringe, late fringe, overnight, or primetime.

48. The system of claim 36, wherein the computing device is further configured to determine an average distance between points of each subset of the plurality of subsets and a center point of a subset of the plurality of subsets of the plurality of devices.

49. A system comprising: a plurality of subsets of devices configured to output content; and a computing device configured to: receive an indication of a type of content and a request for a ranking of a plurality of content time slots; determine, based on a weight for each device of a subset of the plurality of subsets of devices, an index parameter indicative of a likelihood of a device of the plurality of subsets of devices to output the type of content; and send, based on the index parameter, data indicative of the plurality of content time slots.

50. The system of claim 49, wherein the computing device configured to receive the request further comprises the computing device configured to receive, from a user device, an indication of a target device subset corresponding to the type of content, wherein the type of content comprises at least one of: a sports content item, an drama content item, a content item for a chronological age range, a genre content item, or a content item title.

51. The system of claim 49, wherein the computing device configured to receive the request further comprises the computing device configured to receive, from a user device, an indication of the plurality of subsets of devices, wherein the plurality of subsets of devices comprises: a first subset associated with a first quantity of output content that is above at least one subset threshold, a second subset associated with a second quantity of output content that is below the at least one subset threshold, and a third subset associated with a third quantity of output content that is below the second quantity of output content.

52. The system of claim 49, wherein the computing device configured to determine the index parameter further comprises the computing device configured to determine a ratio of a portion of the plurality of subsets of devices associated with the likelihood to output the type of content and a quantity of the plurality of subsets of devices.

53. The system of claim 49, wherein the computing device configured to send the data further comprises the computing device configured to send, based on the type of content, at least one of: a matrix, a list, a spreadsheet, or a table.

54. The system of claim 49, wherein the computing device configured to send the data further comprises the computing device configured to send an indication of a highest ranked content time slot of the plurality of content time slots.

55. The system of claim 49, wherein the computing device configured to send the data further comprises the computing device configured to send an indication of a lowest ranked content time slot of the plurality of content time slots.

56. The system of claim 49, wherein each content time slot of the plurality of content time slots defines a time period and a type of content source.

57. The system of claim 49, wherein the plurality of subsets of devices are associated with at least one of: output of content, selection of a content type, or content viewership.

58. The system of claim 49, wherein the computing device is further configured to cluster, based on viewership data, a portion of the plurality of subsets of devices to a target device subset of a plurality of target device subsets.

59. The system of claim 49, the computing device is further configured to determine a weight for each subset of the plurality of subsets of devices based on an average difference between content output times of devices in each subset and a midpoint of a subset of the plurality of subsets of devices, wherein the midpoint comprises a weighted average content output time of the subset and the weight is based on a distance associated with each subset of the plurality of subsets of devices.

60. The system of claim 49, the computing device is further configured to determine, based on clustering devices according to a clustering algorithm, the plurality of subsets of the devices.

61. A system comprising: a plurality of devices configured to output content; and a computing device configured to: determine a plurality of subsets of the plurality of devices, wherein each subset of the plurality of subsets is associated with a quantity of time associated with output of a type of content; determine, for each subset of the plurality of subsets, based on viewership data associated with each subset of the plurality of subsets, a weight; determine, based on the weight for each subset of the plurality of subsets, an index parameter indicative of a likelihood of a device of the subset of the plurality of devices to output the type of content; and generate, based on the index parameter, data indicative of a ranking of a plurality of content networks.

62. The system of claim 61, wherein the computing device configured to determine the plurality of subsets further comprises the computing device configured to apply a k-means clustering algorithm based on at least one of: a level of viewership, a type of content genre, a type of content network, a type of content program, or a type of content title.

63. The system of claim 61, wherein the computing device configured to determine the plurality of subsets further comprises the computing device configured to cluster, based on audience type, the plurality of devices, wherein the audience type comprises at least one of: a genre audience type, a sports content audience type, a cooking content audience type, a chronological age range audience type, a content network audience type, or a content program audience type.

64. The system of claim 61, wherein the computing device configured to determine the plurality of subsets further comprises the computing device configured to determine at least one subset threshold, wherein the plurality of subsets of the plurality of devices comprises a first subset associated with a first quantity of output content that is above the at least one subset threshold, a second subset associated with a second quantity of output content that is below the at least one subset threshold, and a third subset associated with a third quantity of output content that is below the second quantity of output content.

65. The system of claim 61, wherein the computing device configured to determine the weight for each subset of the plurality of subsets further comprises the computing device configured to: determine a first average distance of points of a first subset of the plurality of subsets to a midpoint of the first subset; and determine a second average distance of points of a second subset of the plurality of subsets to the midpoint.

66. The system of claim 61, wherein the computing device configured to determine the index parameter further comprises the computing device configured to apply, to corresponding index values, the weight for each subset of the plurality of subsets.

67. The system of claim 61, wherein the computing device configured to determine the index parameter further comprises the computing device configured to determine a ratio of a portion of the plurality of devices associated with the likelihood to output the type of content and a quantity of the plurality of devices, wherein the portion of the plurality of devices is associated with viewership of a target device subset during a content time slot, and wherein the quantity of the plurality of devices is associated with viewership by a general audience during the content time slot.

68. The system of claim 61, wherein the computing device configured to generate the data further comprises the computing device configured to generate at least one of: a matrix, a list, a spreadsheet, or a table.

69. The system of claim 61, wherein the computing device configured to generate the data further comprises the computing device configured to generate an indication of a content network of the plurality of content networks during a content time slot, wherein the content time slot comprises at least one of: early morning, daytime, fringe, late fringe, overnight, or primetime.

70. The system of claim 61, wherein the computing device configured to generate the data further comprises the computing device configured to determine a highest ranked content network of the plurality of content networks.